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Apache Airflow is one of the latest open-source projects that have aroused great interest in the developer community. So much so that Google has integrated it in Google Cloud’s stack as the de facto tool for orchestrating their services.
What makes this project so special and why has it been so well received? In this post we will go over its evolution and discuss its main characteristics.
We could say that Big Data and Artificial Intelligence are compatible with all industrial sectors. The advantages data collection and analysis brings to all fields are essential to keep marching towards a more evolved future.
Digitization has not reached all sectors in equal measure. The medical industry, for example, was one of the first sectors to get on board and start using the most advanced technologies. Agriculture, however, has been slower to incorporate digitization into its medium- to long-term plans.
In the last edition of Big Data Spain many of the talks revolved around how Big Data technologies have revolutionized diverse industries such as sports, banking, food, and travel.
Deep Learning with neural networks is currently one of the most promising branches of artificial intelligence. This innovative technology is commonly used in applications such as image recognition, voice recognition and machine translation, among others.
There are several options out there in terms of technologies and libraries, Tensorflow – developed by Google – being the most widespread nowadays.
However, today we are going to focus on PyTorch, an emerging alternative that is quickly gaining traction thanks to its ease of use and other advantages, such as its native ability to run on GPUs, which allows traditionally slow processes such as model training to be accelerated. It is Facebook’s main library for deep learning applications.
Its basic elements are tensors, which can be equated to vectors with one or several dimensions.
Claustrophobic spaces known as ‘cubicles’ are starting to be set up in offices across the region; it will soon become a fever that will spread all over the world.
This is an office model that was born to give workers’ autonomy, but nothing was further from the truth. This claustrophobic idea that conflates being productive with ‘being locked for hours without external influence’ is very frustrating for workers.
Soon, despite its enthusiastic adoption by large corporations, it will be proven to be highly inefficient: Man is a social animal by nature – also at work.
When I was little, every once in a while my mother used to take me with her to work during the holiday season. I remember people working in small offices that could hold 4 to 6 people.
This had its advantages: People who had the same job shared the same space. Its main drawback: an excessive departmentalization, which led to separate work units disconnected from the rest of the company.
Meanwhile, in an office next door there was something that was going to change the world and my life ever after: There I saw a computer for the first time. Back then they were kept hidden away because no-one knew exactly what to do with them… We were still unaware of the coming revolution.
During my first job I saw the famous ‘prairies’. Open spaces had been all the rage for a few years now – but they were not something new. They appeared in some classic movies such as The Apartment (1960) because they were commonplace before cubicles were introduced.
Back then, the purpose of open floor plans was to control workers and optimize the use of costly space.
In the new millennium, the goal was to achieve information transparency and improve communication within companies. However, presenteeism was excessively encouraged, which, it turns out, lowers productivity: a fly at the other side of the prairie was enough to distract people.
When I think about my working life I realize that I have always been interested in workspaces. On this same blog I already went into the current state of workspaces in detail, but going beyond that… Where are we going? How will we work in the future? Let us let our imagination fly.
Google Analytics has been for few years now the go-to marketing tool, not only at the metrics extraction level but also to understand the behaviour of website users.
But Google Analytics is still a huge monster where obtaining specific data is sometimes almost mission impossible, particularly when we are starting to work with this platform.
Last week we already put together a few tips to make using this tool easier. Today we will give you another 5 recommendations that will surely help you to get the most out of Google Analytics.
What company which has some of its business online does not want to get more traffic? No matter its industry, size, turnover… the answer will always be yes. And if the traffic is quality traffic, so much the better.
Google Analytics provides us with a huge amount of data that we can use to increase our organic traffic and improve our ranking in search engines.
However, we must keep in mind that we cannot obtain results overnight. The process of attracting more traffic is going to take time – but the wait will make the results worthwhile.
With the huge amount of information provided by Google Analytics, knowing how to ‘manoeuvre’ through its complex platform and go beyond its basic reports between metrics and settings is essential to finding success.
In this guide we will describe each of Google Analytics’ perspectives and how you can implement them right away on your website to increase its search visibility.
Java Version 8 has brought about big changes for this language. Lambda expressions and streams, which provide the language with functional programming features, stand out among them. But with so many changes, it is easy to miss some details, such as the one we will go over in this post.
What role does Machine Learning play in the Big Data universe? It has undoubtedly entailed taking one step beyond in the world of data. Thus, it is no wonder that many of the talks during this 7th Edition of Big Data Spain versed in one way or another, more or less specifically, on this discipline.
Databricks’ new project (MLflow), the challenges automatic learning faces or how behaviours such as consumption have changed were some of the topics that stood out at Big Data Spain 2018. 
The definition and ideation phase is a very important part of the process of creating digital products and services and around which there is lately a lot of debate given the variety of approaches and results.
The results to be obtained might vary depending on the kind of project, the time, the budget, and the team. Therefore, one size does not fit all.
Processes may range from the most condensed – in 5 days, such as Google Sprint – to the longest – lasting 4 to 7 weeks, like our RPM or Sprint zero – and designers, researchers, analysts, and product, technology and business experts can take part in them
Each process has its own purpose and provides a more or less detailed vision of the product to be built. Goals can range from analyzing the business so as to come up with a wider-scope strategy to obtaining as much information as possible to be able to ideate and define the product in order to subsequently develop it.
One of these methodologies is popularly known as Design Thinking (DT), which was developed and disseminated by the consultancy firm IDEO. In short, it is a framework for ideating and defining products and services whose purpose is to act as a step-by-step guide for designing them in 5 stages: Empathizing, defining, ideating, prototyping, and testing.
Over the past few months I have become increasingly convinced that the world of agilism is losing sight, little by little, of what is really important.
Right now we are at a juncture where agile methods have finally arrived at large Spanish companies and are even talked about in board meetings.
However, this has happened, not so much on its own merit, but thanks in part to the publicity it has received from bank ads which “have done away with managers and departments” and papers from big consultancy firms about “tribes and squads,” which, in my opinion, have barely scratched the surface of what being agile is all about.
This type of contents, albeit very helpful for spreading agilism, also causes agilism to lose its true essence bit by bit.
